Premium
AN OPTIMIZATION APPROACH TO AUTOMATIC GENERIC DOCUMENT SUMMARIZATION
Author(s) -
Alguliev Rasim M.,
Aliguliyev Ramiz M.,
Mehdiyev Chingiz A.
Publication year - 2013
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2012.00437.x
Subject(s) - automatic summarization , computer science , particle swarm optimization , nist , optimization problem , artificial intelligence , nonlinear system , mathematical optimization , data mining , machine learning , algorithm , mathematics , natural language processing , physics , quantum mechanics
In this paper, we have presented an optimization approach to document summarization. The potential of optimization based document summarization models has not been well explored to date. This is partially the difficulty to formulate the criteria used for objective assessment. We modeled document summarization as the linear and nonlinear optimization problems. These models generally attempt simultaneously to balance coverage and diversity in the summary. To solve the optimization problem we developed a novel particle swarm optimization (PSO) algorithm. Experiments showed our linear and nonlinear models produce very competitive results, which significantly outperform the NIST baselines in both years. More important, although linear and nonlinear models are comparable to the top three systems S24, S15, and S12 in the DUC2006, they are even superior to the best participating system in the DUC2005.